In the past decade we have seen huge technological advancements on the world. Due to these advancements, we have become accustomed to mass amounts of data, referred to today as Big Data. Big Data “describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis” (SAS, 2016). Due to this, businesses must organise this data so that it can be used effectively to help them make decisions.
To understand how Big Data is collected, we must identify the epi centres of where data can be collected. These epi centres can be categorised; known as the 4V’s. IBM identifies these as:
Volume – scale of data
Variety – number of sources of data
Velocity – speed we receive data
Veracity – certainty/ quality of data
(IBM, 2016)
As the data is collected, it must be organised and analysed for businesses to be able to use it effectively. This is where predictive analysis and business analytics came about.
Predictive Analysis (PA) is “the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data” (SAS, 2016).
Business Analytics (BA) “encompasses all aspects of the data process to facilitate predictive and/ or causal interference-based business decision making.” In simpler terms, it explains the variables in response to a business outcome or result (Baesens et al, 2016).
The fundamental difference between the two is that predictive analysis is an anticipative method,
predicting when things are going to happen and can be used to automatically do things in real-time, on the foundation of numerical algorithms. Business analytics on the other hand is used for previous data on things such as revenue, profits and customer churn, identifying why things happen and for what reasons (Corporate Technologies, 2013). These two analytic systems can be used hand-in-hand to improve business decision making.
How did Big data become Big?
Over the past century or so, the amount of data that we are processing has grown exponentially. As the introduction states, Big Data is evolved around four key sources. As technology has developed these sources have also grown, generating mass amounts of data.
In 1880, the US census was carried out and it was then estimated that this would take 8 years to complete with the then technology. Thankfully, in 1881, Herman Hollerith invented a tabulating machine which reduced the length of completion time down to 1 year. This was the beginning of what is now known as IBM (Da Cruz, 2011).
The population boom of the 1930’s meant that more data had to be collected which in the 1940’s forced libraries to come up with new storage systems to cope (Winshuttle, 2017 ). In 1940, a mathematical framework was built by Claude Shannon which allowed data to be transmitted across noisy channels. This invention has laid the foundations of the infrastructure we use today for collecting data (Winshuttle, 2017). In 1944, Fremont Rider estimated that libraries were doubling in size every sixteen years (Bell, 1973; p 177). In 1961, Derek Price concluded that the numbers of scientific journals were doubling every 15 years (Press, 2013).
In 1970, the relational database was created by Edgar Codd. This database allowed unstructured data to be stored, but allowed data to be found easily. This database is used for online banking, booking tickets and buying products online today (IBM, 2012).
In 1989, Business Intelligence was defined by Howard Dresner as “Concepts and methods to improve business decision making by using fact-based support systems” (Power, 2003). BI is used to make decisions today, based on data they have collected, as the following section discusses.
In 1995, the internet exploded. This would change two-way communication between businesses, generating further data (Winshuttle,2017). In 1999, predictive analysis was born through Enterprise Resource Planning systems which incorporate finance, HR, inventory and distribution management (Preston, 1999). Kevin Ashton, a British Entrepreneur, also came up with the term Internet of Things, which he describes as computers having the ability to know everything, generating data without us, which enable us to track lots of information. This theory is only beginning to take force, as discussed in later in this article (Ashton, 2009).
In 2001, Doug Laney published an article introducing the 3V’s; volume, velocity and variety, which we still use today as the formulation of Big Data, understood by analytic companies (Laney, 2001)
.
In 2008, an article was published called “Big Data Computing: Creating Revolutionary Breakthroughs in Commerce, Science, and Society”. This article discussed how Big Data would transform the medical industry and the defences and intelligence of world countries. The article also states how Big Data is one of the biggest innovations in the last decade and we are only beginning to see its potential (Bryant, Kastz, Lazowski, 2008).
Importance of Predictive Analytics and Business Analytics
Predictive analytics
Statistical Analysis System (SAS) identifies four areas in which predictive analysis is crucial for competitive businesses:
Detecting Fraud
Improve Operations
Optimise Marketing Campaigns
Reducing Risk
(SAS, 2017)
The detection of fraud has become a prominent issue through digitalisation. The banking industry in particular has increasingly moved towards an online environment, and with 10.5m people using banking apps in March 2015, this number is set to rise (Dakers,2015). Big Data allows for abnormalities to be noticed at real-time, creating a safer, online environment. According to Dakers (2015) British people transfer £2.9bn through apps in a typical week, therefore initiating a priority to use predictive analytics in keeping customers cash safe (SAS, 2017).
Online marketing has grown at an incredible rate, with social media fashioning the foundations for marketers to promote and inspire purchase of their products. Online advertising revenue in the US alone was $59.6bn in 2015, up from $49.5bn in 2014 (Statista, 2016). Through PA, the data that’s been collected and analysed can predict buying behaviours and identify cross-selling techniques that incorporate retention and attraction of customers.
The operation of any business is critical when it comes to managing resources and keeping costs down. It is understood that 75% of executives turn to operations when it comes to cutting costs and becoming leaner (Mckinsey Global Survey, 2009 cited in IBM, 2016). Predictive analysis can identify ways in which to manage inventories and identify surpluses. This analysis enables businesses to find the right balance across their entire supply chain with regards to flexibility, speed and costs involved within inventory management (IBM, 2016).
The above areas come together to reduce the overall risk of the business. If a business refuses to get involved with predictive analytics, it runs the risk of letting down its customers, which has a knock on effect on brand image. Once a business knows what type of individual to target, they can create effective content which increases purchase success, reducing risk of losing money. Operationally, if a business can predict highs and lows in sales, it can reduce risk by ordering and manufacturing the correct amount of products, saving money and
time and use extra cash to invest in other business ideas.
Business Analytics
Business analytics is important in three fundamental areas of business:
Supporting strategic planning
Competitive advantage
Delivers tactical value(Stubbs, 2011)
Within these categories, business analytics uncovers why things happen, what is going to happen next and what is the best course of action, taking into consideration the relevance of this data towards the business itself. The key product that is created through business analytics is value. This means the decisions taken must be aligned with the strategic objectives of the business itself (Stubbs, 2011).
Further, competitive advantage can be achieved when a business combines the 4V’s effectively and understands the trends that occur. Once these trends are identified, they can aid strategic decisions which will provide successful revenues in the long run (Business analytics, 2012). Both Strategic planning and Competitive advantage, together, provide tactical value to the business.
Uses of Big Data, Predictive Analytics and Business Analytics
Decision Making
Big Data’s main use is to improve decision making among business operations internally and externally. For this to occur data must be collected from these variables:
Large Scale Enterprises - ERP
Internet of things - Sensors
Online social graphs – FB, Twitter
Open/ Public data – Weather, Traffic, Maps etc
Mobile devices
These variables provide significant amounts of different types of data. Currently, there are 5 billion mobile devices in the world, in which every action and location is monitored. The world of social media is an increasingly lucrative market for Big Data with around 2 billion people accessing them regularly, leaving an online trail behind them while mobile technology allows our location to be tracked and monitored on a real-time basis (Baesens et al, 2016).
Below are examples of how these variables can be critical for a business’s decision making:
Coca-cola
Coca-cola changed its recipe after 99 years in 1985. It spent millions on market research and on promoting its new Coca-cola however when the product was released it was a flop with Coca-Cola-lovers rejecting the new taste. It wasn’t until 77 days after the launch that they realised people hated it. They hated it so much that Classic Coca-Cola, beat New Coca-cola and Pepsi in the same year (Jain, 2016). “Sometimes consumers have developed a special bond with a product and don’t want to let it go” (Jain, 2016).
This example proves that if social media was available, the length of time it took Coke to realise its consumers didn’t like the product would have been considerably less, which could have saved millions of pounds through understanding how their customers felt about the product.
Proctor and Gamble
A new product, NyQuil was launched as a remedy for flu and cold however from the data that Proctor and Gamble received, consumers were also using it as sleeping aid. With this data, they launched a new product called ZzzQuil, which was the same product, different name, but for a different use, adding millions of dollars to the company’s sales (Jain, 2016).
This type of data identified an opportunity in the market by realising how consumers were using products. Big Data allowed Proctor and Gamble to see this opportunity, act on it, and make a calculated business decision.
The importance of Big Data is crucial in the sales and profit departments of businesses. The success rate of new products is 25%, therefore Big Data is highly important for businesses to understand what consumers are saying (Jain, 2016). The sooner companies find the problems; they can fix them and increase the chances of success as demonstrated through these short examples (Jain, 2016).
Proactive Customer Care
Customer care is based on the idea of giving information when it is required. Modern mobile devices have location-based services installed which allow an individual location to be identified at real-time. Big Data can take advantage of this whereby companies are able to offer eCoupons based on consumers location. This technique and has the ascendency to increase footfall, ultimately increasing their profits (Baesens et al, 2016).
This concept is still relatively new however companies Amazon, Comcast and AT&T are beginning to infiltrate big data into the way they work. Amazon customers get notified if there are problems with streaming services and are thus compensated, even if they were unaware of the problem. Comcast call centres use real-time data to understand why the customer is calling, smooth interactions and direct them towards products based on previous purchases (Neel, 2016). An important evolution from big data is to “know a customer’s problem before they actually know themselves, being able to notify them of a pending issue in advance” (Roberts, 2016 cited in Neel, 2016).
Online to Offline commerce
This idea is yet to make its way into the UK however in China this is a phenomenon. O2O commerce “generates and uses an enormous amount of data, using location-based services, mobile computing activities and Internet of things techniques” (Baesens et al, 2016). An important part of this commerce is that consumers can identify the deals online, but purchase takes place offline.
This commerce idea has the power to change business and profits. In Bangalore, A restaurant was full at the weekends yet quiet on weekdays. Their footfall decreased by one-third of the weekend high. To improve footfall, the restaurant owner decided to reduce the price of products from between 20-40%, driving traffic up 35% (Online to Offline, 2016).
Healthcare
Big Data is changing the way we provide healthcare through the collection of data relating to costs, medications and diseases. The data can be analysed, providing vital information in the form of trends, precursors for diseases and genetic information (Lateef, 2016). This development is likely to be a huge step towards medical care and treatment. These developments could mean that diagnosis is sped up through the use of trends.
Intel is currently working on medical developments within Parkinson’s disease. They have created wearable devices that collect objective data in the form of movements and tremors. This data is monitored 24/7 and is sent to a database allowing analytics and scientists to come together and explore Parkinson’s disease. The sensors involved provide the right data for this type of research and this is safe evidence that this could be a huge development in medical research (Intel, 2016). Current technology is only “scratching the surface” in medical research and the opportunity to develop technologies to improve medical care is ‘boundless’ (Kasabian, cited in Intel, 2016).
Complications of using Big Data
Privacy
Everything we do, whether it is posting or liking on Facebook and Twitter or casually searching Amazon for things to buy, is monitored and therefore is intruding on what used to be our privacy. This data is not only personal; the data that is collected through sign-up pages and profiles can be reused for future purposes as it offers considerable competitive advantage for those companies (Mayer- Schonberger & Cukier, 2013). The issue that comes with this is at the time the data was collected it wasn’t intended to be used again or at least companies didn’t know what it would be used for later on and to gain consent would take too long and just is not possible.
Privacy-enhancing technologies (PET’s) are being developed to try and keep user privacy safe and secure. The likes of Whatsapp and Facebook have developed encrypted messages meaning they are untraceable to authorities and not accessible to third party users. However, these PET’s will never totally provide confidentiality resulting in exoneration ( Hubaux & Juels, 2016).
Probability and Punishment
Predictive analysis allows us to see trends using numerical algorithms to predict and prevent crime-based activities. “The growth of ICT, computerisation and artificial intelligence… serves as momentum for the crime-prevention industry” (Jin-ho & Seung-Ryul, 2016). This information, as it is developed and intensified, could predict many major things. However, Mayer-Schonberger & Cukier claim this to be a major risk as Big Data could bring culpability to a person based on judgments and predictions rather than actions.
In over half of US states, Big data allows ‘predictive policing’ to identify streets, groups and individuals to observe more closely. The US Department of Homeland also use Big Data to identify potential terrorists by monitoring certain activities and behaviour. However with Big Data predicting what is going to happen, it also creates trends and identifies areas of concern. If we start predicting who is going to do what, we could discriminate towards certain groups and certain individuals, creating a cause for violence rather than prevention. We must be respectful of allowing people to live free lives and if they have done nothing wrong as yet, it is against justice to bring someone in for an act they haven’t committed (Mayer- Schonberger & Cukier, 2013).
Current and Future Products
Amazon Echo – ‘Alexa’
The Amazon echo is a voice activated device which can control objects within your house, play music, set meetings, provide information and do much more (Amazon, 2016).
Alexa has skills that allow you to pair up accounts and personalise your experience such as booking flights using bank details, explain a train route or turn on lights and control a thermostat within your house.
This product is an asset to Amazon as they can understand their consumers from a different and unique perspective. The product listens out for what people are saying allowing Alexa to do things straight away, removing human interaction. This provides big data and through predictive and business analytics, Amazon can cross-sell products to each individual person, and tailor their Echo experience to that individual based on what the individual wants.
In the not-so-distant future Alexa will be able to understand trends, through predictive analytics, in how someone goes about their day. These may include knowing when the person leaves the house and their mode of transport. This would allow Alexa to automatically order and purchase train tickets, an Uber or a bus pass without you even asking for it, which replicates what Roberts (2016) said in understanding the customer before they even know themselves. This is capable through the connecting of applications, accounts and services which generates huge amounts of data that can be analysed and processed to form a greater understanding of an individual (Amazon, 2016).
Waymo - Smart Cars
Waymo is the company leading the Google Self-driving car project. This project began in 2009 and since then the self-driving cars have covered over 2 million miles. They have built a new futuristic looking prototype car and in 2015 this prototype drove a passenger for the first time on a public road (Waymo, 2016).
This smart car is powered by the interconnecting of software and sensors which enables the car to detect pedestrians, vehicles, cyclists and more from around ‘two football-fields’ away. The sensors are able to detect motions and activity, the software then tells the car what to do, for example, if a cyclist is moving into the lane, sensors will pick up the use of the left arm indication. The software then is able to tell the car to slow down and allow the cyclist in. In terms of road navigation, these sensors also identify changes in the road such as diversions and road works while the software determines acceptable road positions to avoid blind spots. The working togetherness of these two engineering feats allows for this imaginary idea to become much more of a reality (Waymo, 2016)
Conclusion - New, Trend or Management
Is it New?
Data has been around for centuries but has become more prominent in the last 60 years. The development of technology has seen our ability to collect, retain and analyse data become easier. We are now starting to become accustomed with huge databases and with the growth of social media, sensors and technology. Data is now becoming the norm and it is changing the way we do things, however it is not new as we have known about it for decades now.
Is it a trend?
I think it is clear that the collection of data started off as a trend however it became a big deal around the 1940’s and since, a lot of focus has been put on this ever since. The introduction of technology and the ferocious pace that it has been developed has allowed data to integrate into the way we do things. As the examples show, with Big data, there are so many opportunities and more companies will look to access this information in the future.
Is it management?
Today, Big Data is now a huge part of society, where we are monitored on nearly everything we do, indoors and outdoors. It is this development that has allowed data to become more and more useful to companies and as such, evolved Big Data into a management strategy that is now at the core of many global businesses.
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Introduction
Table of Contents
To understand how Big Data is collected, we must identify the epi centres of where data can be collected. These epi centres can be categorised; known as the 4V’s. IBM identifies these as:
As the data is collected, it must be organised and analysed for businesses to be able to use it effectively. This is where predictive analysis and business analytics came about.
Predictive Analysis (PA) is “the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data” (SAS, 2016).
Business Analytics (BA) “encompasses all aspects of the data process to facilitate predictive and/ or causal interference-based business decision making.” In simpler terms, it explains the variables in response to a business outcome or result (Baesens et al, 2016).
The fundamental difference between the two is that predictive analysis is an anticipative method,
predicting when things are going to happen and can be used to automatically do things in real-time, on the foundation of numerical algorithms. Business analytics on the other hand is used for previous data on things such as revenue, profits and customer churn, identifying why things happen and for what reasons (Corporate Technologies, 2013). These two analytic systems can be used hand-in-hand to improve business decision making.
How did Big data become Big?
Over the past century or so, the amount of data that we are processing has grown exponentially. As the introduction states, Big Data is evolved around four key sources. As technology has developed these sources have also grown, generating mass amounts of data.In 1880, the US census was carried out and it was then estimated that this would take 8 years to complete with the then technology. Thankfully, in 1881, Herman Hollerith invented a tabulating machine which reduced the length of completion time down to 1 year. This was the beginning of what is now known as IBM (Da Cruz, 2011).
The population boom of the 1930’s meant that more data had to be collected which in the 1940’s forced libraries to come up with new storage systems to cope (Winshuttle, 2017 ). In 1940, a mathematical framework was built by Claude Shannon which allowed data to be transmitted across noisy channels. This invention has laid the foundations of the infrastructure we use today for collecting data (Winshuttle, 2017). In 1944, Fremont Rider estimated that libraries were doubling in size every sixteen years (Bell, 1973; p 177). In 1961, Derek Price concluded that the numbers of scientific journals were doubling every 15 years (Press, 2013).
In 1970, the relational database was created by Edgar Codd. This database allowed unstructured data to be stored, but allowed data to be found easily. This database is used for online banking, booking tickets and buying products online today (IBM, 2012).
In 1989, Business Intelligence was defined by Howard Dresner as “Concepts and methods to improve business decision making by using fact-based support systems” (Power, 2003). BI is used to make decisions today, based on data they have collected, as the following section discusses.
In 1995, the internet exploded. This would change two-way communication between businesses, generating further data (Winshuttle,2017). In 1999, predictive analysis was born through Enterprise Resource Planning systems which incorporate finance, HR, inventory and distribution management (Preston, 1999). Kevin Ashton, a British Entrepreneur, also came up with the term Internet of Things, which he describes as computers having the ability to know everything, generating data without us, which enable us to track lots of information. This theory is only beginning to take force, as discussed in later in this article (Ashton, 2009).
In 2001, Doug Laney published an article introducing the 3V’s; volume, velocity and variety, which we still use today as the formulation of Big Data, understood by analytic companies (Laney, 2001)
.
In 2008, an article was published called “Big Data Computing: Creating Revolutionary Breakthroughs in Commerce, Science, and Society”. This article discussed how Big Data would transform the medical industry and the defences and intelligence of world countries. The article also states how Big Data is one of the biggest innovations in the last decade and we are only beginning to see its potential (Bryant, Kastz, Lazowski, 2008).
Importance of Predictive Analytics and Business Analytics
Predictive analytics
Statistical Analysis System (SAS) identifies four areas in which predictive analysis is crucial for competitive businesses:The detection of fraud has become a prominent issue through digitalisation. The banking industry in particular has increasingly moved towards an online environment, and with 10.5m people using banking apps in March 2015, this number is set to rise (Dakers,2015). Big Data allows for abnormalities to be noticed at real-time, creating a safer, online environment. According to Dakers (2015) British people transfer £2.9bn through apps in a typical week, therefore initiating a priority to use predictive analytics in keeping customers cash safe (SAS, 2017).
Online marketing has grown at an incredible rate, with social media fashioning the foundations for marketers to promote and inspire purchase of their products. Online advertising revenue in the US alone was $59.6bn in 2015, up from $49.5bn in 2014 (Statista, 2016). Through PA, the data that’s been collected and analysed can predict buying behaviours and identify cross-selling techniques that incorporate retention and attraction of customers.
The operation of any business is critical when it comes to managing resources and keeping costs down. It is understood that 75% of executives turn to operations when it comes to cutting costs and becoming leaner (Mckinsey Global Survey, 2009 cited in IBM, 2016). Predictive analysis can identify ways in which to manage inventories and identify surpluses. This analysis enables businesses to find the right balance across their entire supply chain with regards to flexibility, speed and costs involved within inventory management (IBM, 2016).
The above areas come together to reduce the overall risk of the business. If a business refuses to get involved with predictive analytics, it runs the risk of letting down its customers, which has a knock on effect on brand image. Once a business knows what type of individual to target, they can create effective content which increases purchase success, reducing risk of losing money. Operationally, if a business can predict highs and lows in sales, it can reduce risk by ordering and manufacturing the correct amount of products, saving money and
time and use extra cash to invest in other business ideas.
Business Analytics
Business analytics is important in three fundamental areas of business:Within these categories, business analytics uncovers why things happen, what is going to happen next and what is the best course of action, taking into consideration the relevance of this data towards the business itself. The key product that is created through business analytics is value. This means the decisions taken must be aligned with the strategic objectives of the business itself (Stubbs, 2011).
Further, competitive advantage can be achieved when a business combines the 4V’s effectively and understands the trends that occur. Once these trends are identified, they can aid strategic decisions which will provide successful revenues in the long run (Business analytics, 2012). Both Strategic planning and Competitive advantage, together, provide tactical value to the business.
Uses of Big Data, Predictive Analytics and Business Analytics
Decision Making
Big Data’s main use is to improve decision making among business operations internally and externally. For this to occur data must be collected from these variables:These variables provide significant amounts of different types of data. Currently, there are 5 billion mobile devices in the world, in which every action and location is monitored. The world of social media is an increasingly lucrative market for Big Data with around 2 billion people accessing them regularly, leaving an online trail behind them while mobile technology allows our location to be tracked and monitored on a real-time basis (Baesens et al, 2016).
Below are examples of how these variables can be critical for a business’s decision making:
Coca-cola
Coca-cola changed its recipe after 99 years in 1985. It spent millions on market research and on promoting its new Coca-cola however when the product was released it was a flop with Coca-Cola-lovers rejecting the new taste. It wasn’t until 77 days after the launch that they realised people hated it. They hated it so much that Classic Coca-Cola, beat New Coca-cola and Pepsi in the same year (Jain, 2016). “Sometimes consumers have developed a special bond with a product and don’t want to let it go” (Jain, 2016).
This example proves that if social media was available, the length of time it took Coke to realise its consumers didn’t like the product would have been considerably less, which could have saved millions of pounds through understanding how their customers felt about the product.
Proctor and Gamble
A new product, NyQuil was launched as a remedy for flu and cold however from the data that Proctor and Gamble received, consumers were also using it as sleeping aid. With this data, they launched a new pr
This type of data identified an opportunity in the market by realising how consumers were using products. Big Data allowed Proctor and Gamble to see this opportunity, act on it, and make a calculated business decision.
The importance of Big Data is crucial in the sales and profit departments of businesses. The success rate of new products is 25%, therefore Big Data is highly important for businesses to understand what consumers are saying (Jain, 2016). The sooner companies find the problems; they can fix them and increase the chances of success as demonstrated through these short examples (Jain, 2016).
Proactive Customer Care
Customer care is based on the idea of giving information when it is required. Modern mobile devices have location-based services installed which allow an individual location to be identified at real-time. Big Data can take advantage of this whereby companies are able to offer eCoupons based on consumers location. This technique and has the ascendency to increase footfall, ultimately increasing their profits (Baesens et al, 2016).
This concept is still relatively new however companies Amazon, Comcast and AT&T are beginning to infiltrate big data into the way they work. Amazon customers get notified if there are problems with streaming services and are thus compensated, even if they were unaware of the problem. Comcast call centres use real-time data to understand why the customer is calling, smooth interactions and direct them towards products based on previous purchases (Neel, 2016). An important evolution from big data is to “know a customer’s problem before they actually know themselves, being able to notify them of a pending issue in advance” (Roberts, 2016 cited in Neel, 2016).
Online to Offline commerce
This idea is yet to make its way into the UK however in China this is a phenomenon. O2O commerce “generates and uses an enormous amount of data, using location-based services, mobile computing activities and Internet of things techniques” (Baesens et al, 2016). An important part of this commerce is that consumers can identify the deals online, but purchase takes place offline.
This commerce idea has the power to change business and profits. In Bangalore, A restaurant was full at the weekends yet quiet on weekdays. Their footfall decreased by one-third of the weekend high. To improve footfall, the restaurant owner decided to reduce the price of products from between 20-40%, driving traffic up 35% (Online to Offline, 2016).
Healthcare
Big Data is changing the way we provide healthcare through the collection of data relating to costs, medications and diseases. The data can be analysed, providing vital information in the form of trends, precursors for diseases and genetic information (Lateef, 2016). This development is likely to be a huge step towards medical care and treatment. These developments could mean that diagnosis is sped up through the use of trends.
Intel is currently working on medical developments within Parkinson’s disease. They have created wearable devices that collect objective data in the form of movements and tremors. This data is monitored 24/7 and is sent to a database allowing analytics and scientists to come together and explore Parkinson’s disease. The sensors involved provide the right data for this type of research and this is safe evidence that this could be a huge development in medical research (Intel, 2016). Current technology is only “scratching the surface” in medical research and the opportunity to develop technologies to improve medical care is ‘boundless’ (Kasabian, cited in Intel, 2016).
Complications of using Big Data
Privacy
Everything we do, whether it is posting or liking on Facebook and Twitter or casually searching Amazon for things to buy, is monitored and therefore is intruding on what used to be our privacy. This data is not only personal; the data that is collected through sign-up pages and profiles can be reused for future purposes as it offers considerable competitive advantage for those companies (Mayer- Schonberger & Cukier, 2013). The issue that comes with this is at the time the data was collected it wasn’t intended to be used again or at least companies didn’t know what it would be used for later on and to gain consent would take too long and just is not possible.
Privacy-enhancing technologies (PET’s) are being developed to try and keep user privacy safe and secure. The likes of Whatsapp and Facebook have developed encrypted messages meaning they are untraceable to authorities and not accessible to third party users. However, these PET’s will never totally provide confidentiality resulting in exoneration ( Hubaux & Juels, 2016).
Probability and Punishment
In over half of US states, Big data allows ‘predictive policing’ to identify streets, groups and individuals to observe more closely. The US Department of Homeland also use Big Data to identify potential terrorists by monitoring certain activities and behaviour. However with Big Data predicting what is going to happen, it also creates trends and identifies areas of concern. If we start predicting who is going to do what, we could discriminate towards certain groups and certain individuals, creating a cause for violence rather than prevention. We must be respectful of allowing people to live free lives and if they have done nothing wrong as yet, it is against justice to bring someone in for an act they haven’t committed (Mayer- Schonberger & Cukier, 2013).
Current and Future Products
Amazon Echo – ‘Alexa’
The Amazon echo is a voice activated device which can control objects within your house, play music, set meetings, provide information and do much more (Amazon, 2016).
Alexa has skills that allow you to pair up accounts and personalise your experience such as booking flights using bank details, explain a train route or turn on lights and control a thermostat within your house.
This product is an asset to Amazon as they can understand their consumers from a different and unique perspective. The product listens out for what people are saying allowing Alexa to do things straight away, removing human interaction. This provides big data and through predictive and business analytics, Amazon can cross-sell products to each individual person, and tailor their Echo experience to that individual based on what the individual wants.
In the not-so-distant future Alexa will be able to understand trends, through predictive analytics, in how someone goes about their day. These may include knowing when the person leaves the house and their mode of transport. This would allow Alexa to automatically order and purchase train tickets, an Uber or a bus pass without you even asking for it, which replicates what Roberts (2016) said in understanding the customer before they even know themselves. This is capable through the connecting of applications, accounts and services which generates huge amounts of data that can be analysed and processed to form a greater understanding of an individual (Amazon, 2016).
Waymo - Smart Cars
Waymo is the company leading the Google Self-driving car project. This project began in 2009 and since then the self-driving cars have covered over 2 million miles. They have built a new futuristic looking prototype car and in 2015 this prototype drove a passenger for the first time on a public road (Waymo, 2016).
This smart car is powered by the interconnecting of software and sensors which enables the car to detect pedestrians, vehicles, cyclists and more from around ‘two football-fields’ away. The sensors are able to detect motions and activity, the software then tells the car what to do, for example, if a cyclist is moving into the lane, sensors will pick up the use of the left arm indication. The software then is able to tell the car to slow down and allow the cyclist in. In terms of road navigation, these sensors also identify changes in the road such as diversions and road works while the software determines acceptable road positions to avoid blind spots. The working togetherness of these two engineering feats allows for this imaginary idea to become much more of a reality (Waymo, 2016)
Conclusion - New, Trend or Management
Is it New?Data has been around for centuries but has become more prominent in the last 60 years. The development of technology has seen our ability to collect, retain and analyse data become easier. We are now starting to become accustomed with huge databases and with the growth of social media, sensors and technology. Data is now becoming the norm and it is changing the way we do things, however it is not new as we have known about it for decades now.
Is it a trend?
I think it is clear that the collection of data started off as a trend however it became a big deal around the 1940’s and since, a lot of focus has been put on this ever since. The introduction of technology and the ferocious pace that it has been developed has allowed data to integrate into the way we do things. As the examples show, with Big data, there are so many opportunities and more companies will look to access this information in the future.
Is it management?
Today, Big Data is now a huge part of society, where we are monitored on nearly everything we do, indoors and outdoors. It is this development that has allowed data to become more and more useful to companies and as such, evolved Big Data into a management strategy that is now at the core of many global businesses.
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